Difference between revisions of "Principal component analysis"

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Line 8: Line 8:
  
 
Operatively, this entails:
 
Operatively, this entails:
 +
; Selecting the input
 +
: a data folder
 +
: a table
 +
: a mask
 
; Computing a cross-correlation matrix
 
; Computing a cross-correlation matrix
 
: this is typically the most consuming part, as it involves to compare all particles in the data folder against all particles.
 
: this is typically the most consuming part, as it involves to compare all particles in the data folder against all particles.
 
; Computing the eigenvalues, eigenvolumes and eigencomponents
 
; Computing the eigenvalues, eigenvolumes and eigencomponents
 
; Using the eigencomponents to create a classification.
 
; Using the eigencomponents to create a classification.

Revision as of 13:26, 18 April 2016

In general, a Principal Component Analysis aims at analyzing a data set and discovering a set of coordinates that capture the most representative features of said data.

In Dynamo, the PCA is the process of finding a reduced set of "eigenvolumes" that allow to approximatively represent each particle in our data set as a combination of these eigenvolumes. Which this representation, a generic particle can be represented by the contributions of each "eigenvolume" to the particle, i.e., by a set of "eigencomponents", normally in a number no much higher than 20.

Once the particles are represent by small sets of scalars, they can be classified with standard methods like k-means.


Operatively, this entails:

Selecting the input
a data folder
a table
a mask
Computing a cross-correlation matrix
this is typically the most consuming part, as it involves to compare all particles in the data folder against all particles.
Computing the eigenvalues, eigenvolumes and eigencomponents
Using the eigencomponents to create a classification.